With the development of Internet technology, it becomes increasingly popular to conduct user transactions (such as product transactions and service transactions) utilizing the Internet. In order to guarantee the safety of conducting transactions utilizing the Internet, it is necessary to identify risky users (e.g., advertisers operating fraudulent websites, merchants selling illegal products, users forging information to defraud loans) and prevent their participation in the transactions.
However, the existing method relies on the identifying by a machine learning model obtained by regularly trained with the user information (e.g., user name, address, email address) This method not only leads to a long renewal period of the machine learning model, but also the prompt modification of the user information to avoid being identified again, when a risky user finds himself being identified. In this regard, there exists a problem that the identifier accuracy is low.